The next section details the design of our benchmarking experiments. We consider two
data-intensive neuroimaging applications: high-resolution imaging,
represented by the BigBrain data [1]}, and large functional
MRI studies, represented by data from the consortium for reliability and
reproducibility (CoRR [2]}). We test application pipelines
involving different patterns (map-only, map-reduce), and different types of
implementations (plain Python, command-line,
containerized). We evaluate performance on a dedicated cluster
representative of the ones used in today's data-intensive neuroimaging
studies, using the main Dask and Spark APIs. The other sections present
our results, discussion, and conclusions.
